1  Skin: QC & Filtering

1.1 Set up Seurat workspace

# Load libraries
library(data.table)
library(devtools)
library(presto)
library(glmGamPoi)
library(sctransform)
library(Seurat)
library(tidyverse)
library(miQC)   
library(SeuratWrappers)
library(flexmix)
library(SingleCellExperiment)
library(SummarizedExperiment)
library(readxl)
library(fishpond)
library(Matrix)
library(speckle)
library(scater)
library(patchwork)
library(vctrs)
library(alevinQC)
library(harmony)
library(scDblFinder)
library(cellXY)

# Set global options for Seurat v5 objects
options(Seurat.object.assay.version = 'v5')

1.2 Load alevin-fry output

Note using U+S+A counts via snRNA

P101_Skin_Post3rd_2.5mgIpi_RNA <- loadFry(fryDir = "/jsimonlab/projects/Wu/Melanoma_scRNA_Eryn/alevinfry_072023/P101_Skin_Post3rd_2.5mgIpi_RNA_alevin_quant_crlikeem/", outputFormat = "snRNA")

locating quant file

Reading meta data

USA mode: TRUE

Processing 62703 genes and 80763 barcodes

Using pre-defined output format: snrna

Building the 'counts' assay, which contains U S A

Constructing output SingleCellExperiment object

Done

P101_Skin_Pre3rd_2.5mgIpi_RNA <- loadFry(fryDir = "/jsimonlab/projects/Wu/Melanoma_scRNA_Eryn/alevinfry_072023/P101_Skin_Pre3rd_2.5mgIpi_RNA_alevin_quant_crlikeem/", outputFormat = "snRNA")

locating quant file

Reading meta data

USA mode: TRUE

Processing 62703 genes and 76442 barcodes

Using pre-defined output format: snrna

Building the 'counts' assay, which contains U S A

Constructing output SingleCellExperiment object

Done

P103_Skin_Post3rd_2.5mgIpi_RNA <- loadFry(fryDir = "/jsimonlab/projects/Wu/Melanoma_scRNA_Eryn/alevinfry_072023/P103_Skin_Post3rd_2.5mgIpi_RNA_alevin_quant_crlikeem/", outputFormat = "snRNA")

locating quant file

Reading meta data

USA mode: TRUE

Processing 62703 genes and 64399 barcodes

Using pre-defined output format: snrna

Building the 'counts' assay, which contains U S A

Constructing output SingleCellExperiment object

Done

P103_Skin_Pre3rd_2.5mgIpi_RNA <- loadFry(fryDir = "/jsimonlab/projects/Wu/Melanoma_scRNA_Eryn/alevinfry_072023/P103_Skin_Pre3rd_2.5mgIpi_RNA_alevin_quant_crlikeem/", outputFormat = "snRNA")

locating quant file

Reading meta data

USA mode: TRUE

Processing 62703 genes and 59420 barcodes

Using pre-defined output format: snrna

Building the 'counts' assay, which contains U S A

Constructing output SingleCellExperiment object

Done

P104_Skin_Post3rd_2.5mgIpi_RNA <- loadFry(fryDir = "/jsimonlab/projects/Wu/Melanoma_scRNA_Eryn/alevinfry_072023/P104_Skin_Post3rd_2.5mgIpi_RNA_fixed020224_alevin_quant_crlikeem/", outputFormat = "snRNA")

locating quant file

Reading meta data

USA mode: TRUE

Processing 62703 genes and 57061 barcodes

Using pre-defined output format: snrna

Building the 'counts' assay, which contains U S A

Constructing output SingleCellExperiment object

Done

P104_Skin_Pre3rd_2.5mgIpi_RNA <- loadFry(fryDir = "/jsimonlab/projects/Wu/Melanoma_scRNA_Eryn/alevinfry_072023/P104_Skin_Pre3rd_2.5mgIpi_RNA_alevin_quant_crlikeem/", outputFormat = "snRNA")

locating quant file

Reading meta data

USA mode: TRUE

Processing 62703 genes and 63314 barcodes

Using pre-defined output format: snrna

Building the 'counts' assay, which contains U S A

Constructing output SingleCellExperiment object

Done

P105_Skin_Post3rd_2.5mgIpi_RNA <- loadFry(fryDir = "/jsimonlab/projects/Wu/Melanoma_scRNA_Eryn/alevinfry_072023/P105_Skin_Post3rd_2.5mgIpi_RNA_alevin_quant_crlikeem/", outputFormat = "snRNA")

locating quant file

Reading meta data

USA mode: TRUE

Processing 62703 genes and 65472 barcodes

Using pre-defined output format: snrna

Building the 'counts' assay, which contains U S A

Constructing output SingleCellExperiment object

Done

P105_Skin_Pre3rd_2.5mgIpi_RNA <- loadFry(fryDir = "/jsimonlab/projects/Wu/Melanoma_scRNA_Eryn/alevinfry_072023/P105_Skin_Pre3rd_2.5mgIpi_RNA_alevin_quant_crlikeem/", outputFormat = "snRNA")

locating quant file

Reading meta data

USA mode: TRUE

Processing 62703 genes and 70513 barcodes

Using pre-defined output format: snrna

Building the 'counts' assay, which contains U S A

Constructing output SingleCellExperiment object

Done

P106_Skin_Post3rd_2.5mgIpi_RNA <- loadFry(fryDir = "/jsimonlab/projects/Wu/Melanoma_scRNA_Eryn/alevinfry_072023/P106_Skin_Post3rd_2.5mgIpi_RNA_alevin_quant_crlikeem/", outputFormat = "snRNA")

locating quant file

Reading meta data

USA mode: TRUE

Processing 62703 genes and 65665 barcodes

Using pre-defined output format: snrna

Building the 'counts' assay, which contains U S A

Constructing output SingleCellExperiment object

Done

P106_Skin_Pre3rd_2.5mgIpi_RNA <- loadFry(fryDir = "/jsimonlab/projects/Wu/Melanoma_scRNA_Eryn/alevinfry_072023/P106_Skin_Pre3rd_2.5mgIpi_RNA_fixed021324_alevin_quant_crlikeem/", outputFormat = "snRNA")

locating quant file

Reading meta data

USA mode: TRUE

Processing 62703 genes and 65342 barcodes

Using pre-defined output format: snrna

Building the 'counts' assay, which contains U S A

Constructing output SingleCellExperiment object

Done

P108_Skin_Post3rd_5mgIpi_RNA <- loadFry(fryDir = "/jsimonlab/projects/Wu/Melanoma_scRNA_Eryn/alevinfry_072023/P108_Skin_Post3rd_5mgIpi_RNA_alevin_quant_crlikeem/", outputFormat = "snRNA")

locating quant file

Reading meta data

USA mode: TRUE

Processing 62703 genes and 69381 barcodes

Using pre-defined output format: snrna

Building the 'counts' assay, which contains U S A

Constructing output SingleCellExperiment object

Done

P108_Skin_Pre3rd_5mgIpi_RNA <- loadFry(fryDir = "/jsimonlab/projects/Wu/Melanoma_scRNA_Eryn/alevinfry_072023/P108_Skin_Pre3rd_5mgIpi_RNA_alevin_quant_crlikeem/", outputFormat = "snRNA")

locating quant file

Reading meta data

USA mode: TRUE

Processing 62703 genes and 69324 barcodes

Using pre-defined output format: snrna

Building the 'counts' assay, which contains U S A

Constructing output SingleCellExperiment object

Done

P109_Skin_Post3rd_5mgIpi_RNA <- loadFry(fryDir = "/jsimonlab/projects/Wu/Melanoma_scRNA_Eryn/alevinfry_072023/P109_Skin_Post3rd_5mgIpi_RNA_alevin_quant_crlikeem/", outputFormat = "snRNA")

locating quant file

Reading meta data

USA mode: TRUE

Processing 62703 genes and 71971 barcodes

Using pre-defined output format: snrna

Building the 'counts' assay, which contains U S A

Constructing output SingleCellExperiment object

Done

P109_Skin_Pre3rd_5mgIpi_RNA <- loadFry(fryDir = "/jsimonlab/projects/Wu/Melanoma_scRNA_Eryn/alevinfry_072023/P109_Skin_Pre3rd_5mgIpi_RNA_alevin_quant_crlikeem/", outputFormat = "snRNA")

locating quant file

Reading meta data

USA mode: TRUE

Processing 62703 genes and 75117 barcodes

Using pre-defined output format: snrna

Building the 'counts' assay, which contains U S A

Constructing output SingleCellExperiment object

Done

P110_Skin_Post3rd_5mgIpi_RNA <- loadFry(fryDir = "/jsimonlab/projects/Wu/Melanoma_scRNA_Eryn/alevinfry_072023/P110_Skin_Post3rd_5mgIpi_RNA_alevin_quant_crlikeem/", outputFormat = "snRNA")

locating quant file

Reading meta data

USA mode: TRUE

Processing 62703 genes and 66843 barcodes

Using pre-defined output format: snrna

Building the 'counts' assay, which contains U S A

Constructing output SingleCellExperiment object

Done

P110_Skin_Pre3rd_5mgIpi_RNA <- loadFry(fryDir = "/jsimonlab/projects/Wu/Melanoma_scRNA_Eryn/alevinfry_072023/P110_Skin_Pre3rd_5mgIpi_RNA_alevin_quant_crlikeem/", outputFormat = "snRNA")

locating quant file

Reading meta data

USA mode: TRUE

Processing 62703 genes and 45085 barcodes

Using pre-defined output format: snrna

Building the 'counts' assay, which contains U S A

Constructing output SingleCellExperiment object

Done

P111_Skin_Post3rd_5mgIpi_RNA <- loadFry(fryDir = "/jsimonlab/projects/Wu/Melanoma_scRNA_Eryn/alevinfry_072023/P111_Skin_Post3rd_5mgIpi_RNA_alevin_quant_crlikeem/", outputFormat = "snRNA")

locating quant file

Reading meta data

USA mode: TRUE

Processing 62703 genes and 63149 barcodes

Using pre-defined output format: snrna

Building the 'counts' assay, which contains U S A

Constructing output SingleCellExperiment object

Done

P111_Skin_Pre3rd_5mgIpi_RNA <- loadFry(fryDir = "/jsimonlab/projects/Wu/Melanoma_scRNA_Eryn/alevinfry_072023/P111_Skin_Pre3rd_5mgIpi_RNA_alevin_quant_crlikeem/", outputFormat = "snRNA")

locating quant file

Reading meta data

USA mode: TRUE

Processing 62703 genes and 67303 barcodes

Using pre-defined output format: snrna

Building the 'counts' assay, which contains U S A

Constructing output SingleCellExperiment object

Done

1.3 Convert gene IDs to gene symbols, collapse transcripts to gene symbols

tx2gene <- read.table("/jsimonlab/genomes/hg38/gencode.v43.annotation_splici/gene_id_to_name.tsv",header=F,sep="\t",col.names=c("tx","gene"))

# Use rownames of first object to apply to all
exp.txId <- rownames(P101_Skin_Post3rd_2.5mgIpi_RNA)
exp.geneId <- as.vector(tx2gene$gene[match(exp.txId, tx2gene$tx)])
exp.tx.grp <- t(sparse.model.matrix(~ 0 + exp.geneId))


P101_Skin_Post3rd_2.5mgIpi_RNA.summarized <- exp.tx.grp %*% counts(P101_Skin_Post3rd_2.5mgIpi_RNA)
rownames(P101_Skin_Post3rd_2.5mgIpi_RNA.summarized) <- rownames(P101_Skin_Post3rd_2.5mgIpi_RNA.summarized) %>% str_replace_all(".+.geneId","")

P101_Skin_Pre3rd_2.5mgIpi_RNA.summarized <- exp.tx.grp %*% counts(P101_Skin_Pre3rd_2.5mgIpi_RNA)
rownames(P101_Skin_Pre3rd_2.5mgIpi_RNA.summarized) <- rownames(P101_Skin_Pre3rd_2.5mgIpi_RNA.summarized) %>% str_replace_all(".+.geneId","")

P103_Skin_Post3rd_2.5mgIpi_RNA.summarized <- exp.tx.grp %*% counts(P103_Skin_Post3rd_2.5mgIpi_RNA)
rownames(P103_Skin_Post3rd_2.5mgIpi_RNA.summarized) <- rownames(P103_Skin_Post3rd_2.5mgIpi_RNA.summarized) %>% str_replace_all(".+.geneId","")

P103_Skin_Pre3rd_2.5mgIpi_RNA.summarized <- exp.tx.grp %*% counts(P103_Skin_Pre3rd_2.5mgIpi_RNA)
rownames(P103_Skin_Pre3rd_2.5mgIpi_RNA.summarized) <- rownames(P103_Skin_Pre3rd_2.5mgIpi_RNA.summarized) %>% str_replace_all(".+.geneId","")

P104_Skin_Post3rd_2.5mgIpi_RNA.summarized <- exp.tx.grp %*% counts(P104_Skin_Post3rd_2.5mgIpi_RNA)
rownames(P104_Skin_Post3rd_2.5mgIpi_RNA.summarized) <- rownames(P104_Skin_Post3rd_2.5mgIpi_RNA.summarized) %>% str_replace_all(".+.geneId","")

P104_Skin_Pre3rd_2.5mgIpi_RNA.summarized <- exp.tx.grp %*% counts(P104_Skin_Pre3rd_2.5mgIpi_RNA)
rownames(P104_Skin_Pre3rd_2.5mgIpi_RNA.summarized) <- rownames(P104_Skin_Pre3rd_2.5mgIpi_RNA.summarized) %>% str_replace_all(".+.geneId","")

P105_Skin_Post3rd_2.5mgIpi_RNA.summarized <- exp.tx.grp %*% counts(P105_Skin_Post3rd_2.5mgIpi_RNA)
rownames(P105_Skin_Post3rd_2.5mgIpi_RNA.summarized) <- rownames(P105_Skin_Post3rd_2.5mgIpi_RNA.summarized) %>% str_replace_all(".+.geneId","")

P105_Skin_Pre3rd_2.5mgIpi_RNA.summarized <- exp.tx.grp %*% counts(P105_Skin_Pre3rd_2.5mgIpi_RNA)
rownames(P105_Skin_Pre3rd_2.5mgIpi_RNA.summarized) <- rownames(P105_Skin_Pre3rd_2.5mgIpi_RNA.summarized) %>% str_replace_all(".+.geneId","")

P106_Skin_Post3rd_2.5mgIpi_RNA.summarized <- exp.tx.grp %*% counts(P106_Skin_Post3rd_2.5mgIpi_RNA)
rownames(P106_Skin_Post3rd_2.5mgIpi_RNA.summarized) <- rownames(P106_Skin_Post3rd_2.5mgIpi_RNA.summarized) %>% str_replace_all(".+.geneId","")

P106_Skin_Pre3rd_2.5mgIpi_RNA.summarized <- exp.tx.grp %*% counts(P106_Skin_Pre3rd_2.5mgIpi_RNA)
rownames(P106_Skin_Pre3rd_2.5mgIpi_RNA.summarized) <- rownames(P106_Skin_Pre3rd_2.5mgIpi_RNA.summarized) %>% str_replace_all(".+.geneId","")

P108_Skin_Post3rd_5mgIpi_RNA.summarized <- exp.tx.grp %*% counts(P108_Skin_Post3rd_5mgIpi_RNA)
rownames(P108_Skin_Post3rd_5mgIpi_RNA.summarized) <- rownames(P108_Skin_Post3rd_5mgIpi_RNA.summarized) %>% str_replace_all(".+.geneId","")

P108_Skin_Pre3rd_5mgIpi_RNA.summarized <- exp.tx.grp %*% counts(P108_Skin_Pre3rd_5mgIpi_RNA)
rownames(P108_Skin_Pre3rd_5mgIpi_RNA.summarized) <- rownames(P108_Skin_Pre3rd_5mgIpi_RNA.summarized) %>% str_replace_all(".+.geneId","")

P109_Skin_Post3rd_5mgIpi_RNA.summarized <- exp.tx.grp %*% counts(P109_Skin_Post3rd_5mgIpi_RNA)
rownames(P109_Skin_Post3rd_5mgIpi_RNA.summarized) <- rownames(P109_Skin_Post3rd_5mgIpi_RNA.summarized) %>% str_replace_all(".+.geneId","")

P109_Skin_Pre3rd_5mgIpi_RNA.summarized <- exp.tx.grp %*% counts(P109_Skin_Pre3rd_5mgIpi_RNA)
rownames(P109_Skin_Pre3rd_5mgIpi_RNA.summarized) <- rownames(P109_Skin_Pre3rd_5mgIpi_RNA.summarized) %>% str_replace_all(".+.geneId","")

P110_Skin_Post3rd_5mgIpi_RNA.summarized <- exp.tx.grp %*% counts(P110_Skin_Post3rd_5mgIpi_RNA)
rownames(P110_Skin_Post3rd_5mgIpi_RNA.summarized) <- rownames(P110_Skin_Post3rd_5mgIpi_RNA.summarized) %>% str_replace_all(".+.geneId","")

P110_Skin_Pre3rd_5mgIpi_RNA.summarized <- exp.tx.grp %*% counts(P110_Skin_Pre3rd_5mgIpi_RNA)
rownames(P110_Skin_Pre3rd_5mgIpi_RNA.summarized) <- rownames(P110_Skin_Pre3rd_5mgIpi_RNA.summarized) %>% str_replace_all(".+.geneId","")

P111_Skin_Post3rd_5mgIpi_RNA.summarized <- exp.tx.grp %*% counts(P111_Skin_Post3rd_5mgIpi_RNA)
rownames(P111_Skin_Post3rd_5mgIpi_RNA.summarized) <- rownames(P111_Skin_Post3rd_5mgIpi_RNA.summarized) %>% str_replace_all(".+.geneId","")

P111_Skin_Pre3rd_5mgIpi_RNA.summarized <- exp.tx.grp %*% counts(P111_Skin_Pre3rd_5mgIpi_RNA)
rownames(P111_Skin_Pre3rd_5mgIpi_RNA.summarized) <- rownames(P111_Skin_Pre3rd_5mgIpi_RNA.summarized) %>% str_replace_all(".+.geneId","")

1.4 Create Seurat objects

P101_Skin_Post3rd_2.5mgIpi_RNA.seurat <- CreateSeuratObject(P101_Skin_Post3rd_2.5mgIpi_RNA.summarized)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

P101_Skin_Pre3rd_2.5mgIpi_RNA.seurat <- CreateSeuratObject(P101_Skin_Pre3rd_2.5mgIpi_RNA.summarized)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

P103_Skin_Post3rd_2.5mgIpi_RNA.seurat <- CreateSeuratObject(P103_Skin_Post3rd_2.5mgIpi_RNA.summarized)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

P103_Skin_Pre3rd_2.5mgIpi_RNA.seurat <- CreateSeuratObject(P103_Skin_Pre3rd_2.5mgIpi_RNA.summarized)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

P104_Skin_Post3rd_2.5mgIpi_RNA.seurat <- CreateSeuratObject(P104_Skin_Post3rd_2.5mgIpi_RNA.summarized)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

P104_Skin_Pre3rd_2.5mgIpi_RNA.seurat <- CreateSeuratObject(P104_Skin_Pre3rd_2.5mgIpi_RNA.summarized)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

P105_Skin_Post3rd_2.5mgIpi_RNA.seurat <- CreateSeuratObject(P105_Skin_Post3rd_2.5mgIpi_RNA.summarized)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

P105_Skin_Pre3rd_2.5mgIpi_RNA.seurat <- CreateSeuratObject(P105_Skin_Pre3rd_2.5mgIpi_RNA.summarized)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

P106_Skin_Post3rd_2.5mgIpi_RNA.seurat <- CreateSeuratObject(P106_Skin_Post3rd_2.5mgIpi_RNA.summarized)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

P106_Skin_Pre3rd_2.5mgIpi_RNA.seurat <- CreateSeuratObject(P106_Skin_Pre3rd_2.5mgIpi_RNA.summarized)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

P108_Skin_Post3rd_5mgIpi_RNA.seurat <- CreateSeuratObject(P108_Skin_Post3rd_5mgIpi_RNA.summarized)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

P108_Skin_Pre3rd_5mgIpi_RNA.seurat <- CreateSeuratObject(P108_Skin_Pre3rd_5mgIpi_RNA.summarized)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

P109_Skin_Post3rd_5mgIpi_RNA.seurat <- CreateSeuratObject(P109_Skin_Post3rd_5mgIpi_RNA.summarized)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

P109_Skin_Pre3rd_5mgIpi_RNA.seurat <- CreateSeuratObject(P109_Skin_Pre3rd_5mgIpi_RNA.summarized)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

P110_Skin_Post3rd_5mgIpi_RNA.seurat <- CreateSeuratObject(P110_Skin_Post3rd_5mgIpi_RNA.summarized)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

P110_Skin_Pre3rd_5mgIpi_RNA.seurat <- CreateSeuratObject(P110_Skin_Pre3rd_5mgIpi_RNA.summarized)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

P111_Skin_Post3rd_5mgIpi_RNA.seurat <- CreateSeuratObject(P111_Skin_Post3rd_5mgIpi_RNA.summarized)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

P111_Skin_Pre3rd_5mgIpi_RNA.seurat <- CreateSeuratObject(P111_Skin_Pre3rd_5mgIpi_RNA.summarized)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

1.5 Add sample name prefix to CBs

P101_Skin_Post3rd_2.5mgIpi_RNA.seurat <- RenameCells(P101_Skin_Post3rd_2.5mgIpi_RNA.seurat,add.cell.id = "P101_Skin_Post3rd_2.5mgIpi_RNA")
P101_Skin_Pre3rd_2.5mgIpi_RNA.seurat <- RenameCells(P101_Skin_Pre3rd_2.5mgIpi_RNA.seurat,add.cell.id = "P101_Skin_Pre3rd_2.5mgIpi_RNA")
P103_Skin_Post3rd_2.5mgIpi_RNA.seurat <- RenameCells(P103_Skin_Post3rd_2.5mgIpi_RNA.seurat,add.cell.id = "P103_Skin_Post3rd_2.5mgIpi_RNA")
P103_Skin_Pre3rd_2.5mgIpi_RNA.seurat <- RenameCells(P103_Skin_Pre3rd_2.5mgIpi_RNA.seurat,add.cell.id = "P103_Skin_Pre3rd_2.5mgIpi_RNA")
P104_Skin_Post3rd_2.5mgIpi_RNA.seurat <- RenameCells(P104_Skin_Post3rd_2.5mgIpi_RNA.seurat,add.cell.id = "P104_Skin_Post3rd_2.5mgIpi_RNA")
P104_Skin_Pre3rd_2.5mgIpi_RNA.seurat <- RenameCells(P104_Skin_Pre3rd_2.5mgIpi_RNA.seurat,add.cell.id = "P104_Skin_Pre3rd_2.5mgIpi_RNA")
P105_Skin_Post3rd_2.5mgIpi_RNA.seurat <- RenameCells(P105_Skin_Post3rd_2.5mgIpi_RNA.seurat,add.cell.id = "P105_Skin_Post3rd_2.5mgIpi_RNA")
P105_Skin_Pre3rd_2.5mgIpi_RNA.seurat <- RenameCells(P105_Skin_Pre3rd_2.5mgIpi_RNA.seurat,add.cell.id = "P105_Skin_Pre3rd_2.5mgIpi_RNA")
P106_Skin_Post3rd_2.5mgIpi_RNA.seurat <- RenameCells(P106_Skin_Post3rd_2.5mgIpi_RNA.seurat,add.cell.id = "P106_Skin_Post3rd_2.5mgIpi_RNA")
P106_Skin_Pre3rd_2.5mgIpi_RNA.seurat <- RenameCells(P106_Skin_Pre3rd_2.5mgIpi_RNA.seurat,add.cell.id = "P106_Skin_Pre3rd_2.5mgIpi_RNA")
P108_Skin_Post3rd_5mgIpi_RNA.seurat <- RenameCells(P108_Skin_Post3rd_5mgIpi_RNA.seurat,add.cell.id = "P108_Skin_Post3rd_5mgIpi_RNA")
P108_Skin_Pre3rd_5mgIpi_RNA.seurat <- RenameCells(P108_Skin_Pre3rd_5mgIpi_RNA.seurat,add.cell.id = "P108_Skin_Pre3rd_5mgIpi_RNA")
P109_Skin_Post3rd_5mgIpi_RNA.seurat <- RenameCells(P109_Skin_Post3rd_5mgIpi_RNA.seurat,add.cell.id = "P109_Skin_Post3rd_5mgIpi_RNA")
P109_Skin_Pre3rd_5mgIpi_RNA.seurat <- RenameCells(P109_Skin_Pre3rd_5mgIpi_RNA.seurat,add.cell.id = "P109_Skin_Pre3rd_5mgIpi_RNA")
P110_Skin_Post3rd_5mgIpi_RNA.seurat <- RenameCells(P110_Skin_Post3rd_5mgIpi_RNA.seurat,add.cell.id = "P110_Skin_Post3rd_5mgIpi_RNA")
P110_Skin_Pre3rd_5mgIpi_RNA.seurat <- RenameCells(P110_Skin_Pre3rd_5mgIpi_RNA.seurat,add.cell.id = "P110_Skin_Pre3rd_5mgIpi_RNA")
P111_Skin_Post3rd_5mgIpi_RNA.seurat <- RenameCells(P111_Skin_Post3rd_5mgIpi_RNA.seurat,add.cell.id = "P111_Skin_Post3rd_5mgIpi_RNA")
P111_Skin_Pre3rd_5mgIpi_RNA.seurat <- RenameCells(P111_Skin_Pre3rd_5mgIpi_RNA.seurat,add.cell.id = "P111_Skin_Pre3rd_5mgIpi_RNA")

1.6 Merge objects

P109_Skin_Post3rd was removed due to low viable cell count

merged.18279.skin <- merge(x = P101_Skin_Pre3rd_2.5mgIpi_RNA.seurat, y=c(P101_Skin_Post3rd_2.5mgIpi_RNA.seurat, P103_Skin_Pre3rd_2.5mgIpi_RNA.seurat, P103_Skin_Post3rd_2.5mgIpi_RNA.seurat, P104_Skin_Post3rd_2.5mgIpi_RNA.seurat, P104_Skin_Pre3rd_2.5mgIpi_RNA.seurat, P105_Skin_Post3rd_2.5mgIpi_RNA.seurat, P105_Skin_Pre3rd_2.5mgIpi_RNA.seurat, P106_Skin_Pre3rd_2.5mgIpi_RNA.seurat, P106_Skin_Post3rd_2.5mgIpi_RNA.seurat, P108_Skin_Pre3rd_5mgIpi_RNA.seurat, P108_Skin_Post3rd_5mgIpi_RNA.seurat, P109_Skin_Pre3rd_5mgIpi_RNA.seurat, P110_Skin_Pre3rd_5mgIpi_RNA.seurat, P110_Skin_Post3rd_5mgIpi_RNA.seurat, P111_Skin_Pre3rd_5mgIpi_RNA.seurat, P111_Skin_Post3rd_5mgIpi_RNA.seurat))
dim(merged.18279.skin)

[1]   61217 1124593

1.7 QC filter

Use relatively loose initial filters

merged.18279.skin <- subset(merged.18279.skin, subset = nCount_RNA > 500 & nFeature_RNA > 250)
dim(merged.18279.skin)

[1] 61217 64133

merged.18279.skin <- PercentageFeatureSet(merged.18279.skin, pattern = "^MT-", col.name = "percent.mt")
merged.18279.skin <- RunMiQC(merged.18279.skin, 
                        percent.mt = "percent.mt", 
                        nFeature_RNA = "nFeature_RNA", 
                        posterior.cutoff = 0.6, 
                        model.slot = "flexmix_model")

Warning: Adding a command log without an assay associated with it

merged.18279.skin <- subset(merged.18279.skin, miQC.keep == "keep")

dim(merged.18279.skin)

[1] 61217 46891

data.frame(table(str_replace_all(colnames(merged.18279.skin),"_RNA_.+","")))

                         Var1 Freq
1  P101_Skin_Post3rd_2.5mgIpi 2338
2   P101_Skin_Pre3rd_2.5mgIpi  622
3  P103_Skin_Post3rd_2.5mgIpi 5340
4   P103_Skin_Pre3rd_2.5mgIpi 1214
5  P104_Skin_Post3rd_2.5mgIpi 7396
6   P104_Skin_Pre3rd_2.5mgIpi 4291
7  P105_Skin_Post3rd_2.5mgIpi 4668
8   P105_Skin_Pre3rd_2.5mgIpi 3341
9  P106_Skin_Post3rd_2.5mgIpi 2214
10  P106_Skin_Pre3rd_2.5mgIpi 2223
11   P108_Skin_Post3rd_5mgIpi 3601
12    P108_Skin_Pre3rd_5mgIpi 1277
13    P109_Skin_Pre3rd_5mgIpi 2144
14   P110_Skin_Post3rd_5mgIpi 1979
15    P110_Skin_Pre3rd_5mgIpi 1593
16   P111_Skin_Post3rd_5mgIpi 1949
17    P111_Skin_Pre3rd_5mgIpi  701

1.8 Add meta data

patient <- str_split_i(colnames(merged.18279.skin),"_",1)
site <- str_split_i(colnames(merged.18279.skin),"_",2)
timepoint <- str_split_i(colnames(merged.18279.skin),"_",3)
IpiCohort <- str_split_i(colnames(merged.18279.skin),"_",4)
assay <- str_split_i(colnames(merged.18279.skin),"_",5)
barcode <- str_split_i(colnames(merged.18279.skin),"_",6)
sample <- paste0(patient,"_",site,"_",timepoint,"_",IpiCohort,"_",assay)

merged.18279.skin <- AddMetaData(merged.18279.skin, patient, col.name="Patient")
merged.18279.skin <- AddMetaData(merged.18279.skin, site, col.name="Site")
merged.18279.skin <- AddMetaData(merged.18279.skin, timepoint, col.name="Timepoint")
merged.18279.skin <- AddMetaData(merged.18279.skin, IpiCohort, col.name="IpiCohort")
merged.18279.skin <- AddMetaData(merged.18279.skin, assay, col.name="Assay")
merged.18279.skin <- AddMetaData(merged.18279.skin, sample, col.name="Sample")

1.9 Join then re-split RNA counts layers by Sample

merged.18279.skin[['RNA']] <- JoinLayers(merged.18279.skin[['RNA']])
merged.18279.skin[["RNA"]] <- split(merged.18279.skin[["RNA"]], f = merged.18279.skin$Sample)

1.10 Save merged object

saveRDS(merged.18279.skin,"Skin_scRNA_Part1.rds")

1.11 Get session info

sessionInfo()

R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Rocky Linux 8.10 (Green Obsidian)

Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblasp-r0.3.15.so;  LAPACK version 3.9.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: America/New_York
tzcode source: system (glibc)

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] cellXY_0.99.0               scDblFinder_1.14.0         
 [3] harmony_1.2.0               alevinQC_1.16.1            
 [5] vctrs_0.6.5                 patchwork_1.3.0            
 [7] scater_1.30.1               scuttle_1.12.0             
 [9] speckle_1.0.0               Matrix_1.6-4               
[11] fishpond_2.6.2              readxl_1.4.3               
[13] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
[15] Biobase_2.62.0              GenomicRanges_1.54.1       
[17] GenomeInfoDb_1.38.8         IRanges_2.36.0             
[19] S4Vectors_0.40.2            BiocGenerics_0.48.1        
[21] MatrixGenerics_1.14.0       matrixStats_1.4.1          
[23] flexmix_2.3-19              lattice_0.22-6             
[25] SeuratWrappers_0.3.19       miQC_1.8.0                 
[27] lubridate_1.9.3             forcats_1.0.0              
[29] stringr_1.5.1               dplyr_1.1.4                
[31] purrr_1.0.2                 readr_2.1.5                
[33] tidyr_1.3.1                 tibble_3.2.1               
[35] ggplot2_3.5.1               tidyverse_2.0.0            
[37] Seurat_5.1.0                SeuratObject_5.0.2         
[39] sp_2.1-4                    sctransform_0.4.1          
[41] glmGamPoi_1.12.2            presto_1.0.0               
[43] Rcpp_1.0.13-1               devtools_2.4.5             
[45] usethis_3.0.0               data.table_1.16.2          

loaded via a namespace (and not attached):
  [1] fs_1.6.5                  spatstat.sparse_3.1-0    
  [3] bitops_1.0-9              httr_1.4.7               
  [5] RColorBrewer_1.1-3        profvis_0.4.0            
  [7] tools_4.3.2               utf8_1.2.4               
  [9] R6_2.5.1                  DT_0.33                  
 [11] lazyeval_0.2.2            uwot_0.2.2               
 [13] urlchecker_1.0.1          withr_3.0.2              
 [15] GGally_2.2.1              gridExtra_2.3            
 [17] progressr_0.15.1          cli_3.6.3                
 [19] spatstat.explore_3.2-6    fastDummies_1.7.3        
 [21] spatstat.data_3.1-4       ggridges_0.5.6           
 [23] pbapply_1.7-2             Rsamtools_2.18.0         
 [25] R.utils_2.12.3            parallelly_1.39.0        
 [27] sessioninfo_1.2.2         limma_3.58.1             
 [29] RSQLite_2.3.8             BiocIO_1.12.0            
 [31] generics_0.1.3            gtools_3.9.5             
 [33] ica_1.0-3                 spatstat.random_3.2-2    
 [35] ggbeeswarm_0.7.2          fansi_1.0.6              
 [37] abind_1.4-8               R.methodsS3_1.8.2        
 [39] lifecycle_1.0.4           yaml_2.3.10              
 [41] edgeR_4.0.16              SparseArray_1.2.2        
 [43] Rtsne_0.17                blob_1.2.4               
 [45] grid_4.3.2                dqrng_0.4.1              
 [47] promises_1.3.0            crayon_1.5.3             
 [49] shinydashboard_0.7.2      miniUI_0.1.1.1           
 [51] beachmat_2.18.1           cowplot_1.1.3            
 [53] KEGGREST_1.42.0           metapod_1.10.1           
 [55] pillar_1.9.0              knitr_1.45               
 [57] rjson_0.2.23              xgboost_1.7.8.1          
 [59] future.apply_1.11.3       codetools_0.2-20         
 [61] leiden_0.4.3.1            glue_1.8.0               
 [63] remotes_2.5.0             png_0.1-8                
 [65] spam_2.11-0               org.Mm.eg.db_3.18.0      
 [67] cellranger_1.1.0          gtable_0.3.6             
 [69] cachem_1.1.0              xfun_0.49                
 [71] S4Arrays_1.2.0            mime_0.12                
 [73] survival_3.7-0            bluster_1.12.0           
 [75] statmod_1.5.0             ellipsis_0.3.2           
 [77] fitdistrplus_1.2-1        ROCR_1.0-11              
 [79] nlme_3.1-166              bit64_4.5.2              
 [81] RcppAnnoy_0.0.22          irlba_2.3.5.1            
 [83] vipor_0.4.7               KernSmooth_2.23-24       
 [85] DBI_1.2.3                 colorspace_2.1-1         
 [87] nnet_7.3-19               tidyselect_1.2.1         
 [89] bit_4.5.0                 compiler_4.3.2           
 [91] BiocNeighbors_1.20.2      DelayedArray_0.28.0      
 [93] plotly_4.10.4             rtracklayer_1.62.0       
 [95] scales_1.3.0              lmtest_0.9-40            
 [97] digest_0.6.37             goftest_1.2-3            
 [99] spatstat.utils_3.1-1      rmarkdown_2.29           
[101] XVector_0.42.0            htmltools_0.5.8.1        
[103] pkgconfig_2.0.3           sparseMatrixStats_1.14.0 
[105] fastmap_1.2.0             rlang_1.1.4              
[107] htmlwidgets_1.6.4         shiny_1.9.1              
[109] DelayedMatrixStats_1.24.0 farver_2.1.2             
[111] zoo_1.8-12                jsonlite_1.8.9           
[113] BiocParallel_1.36.0       R.oo_1.27.0              
[115] BiocSingular_1.18.0       RCurl_1.98-1.16          
[117] magrittr_2.0.3            modeltools_0.2-23        
[119] GenomeInfoDbData_1.2.11   dotCall64_1.2            
[121] munsell_0.5.1             viridis_0.6.5            
[123] reticulate_1.35.0         stringi_1.8.4            
[125] zlibbioc_1.48.2           MASS_7.3-60.0.1          
[127] org.Hs.eg.db_3.18.0       plyr_1.8.9               
[129] pkgbuild_1.4.5            ggstats_0.7.0            
[131] parallel_4.3.2            listenv_0.9.1            
[133] ggrepel_0.9.6             deldir_2.0-4             
[135] Biostrings_2.70.3         splines_4.3.2            
[137] tensor_1.5                hms_1.1.3                
[139] locfit_1.5-9.10           igraph_2.1.1             
[141] spatstat.geom_3.2-8       RcppHNSW_0.6.0           
[143] reshape2_1.4.4            ScaledMatrix_1.10.0      
[145] pkgload_1.4.0             XML_3.99-0.17            
[147] evaluate_1.0.1            scran_1.30.2             
[149] BiocManager_1.30.25       tzdb_0.4.0               
[151] httpuv_1.6.15             RANN_2.6.2               
[153] polyclip_1.10-7           future_1.34.0            
[155] scattermore_1.2           rsvd_1.0.5               
[157] xtable_1.8-4              restfulr_0.0.15          
[159] svMisc_1.2.3              RSpectra_0.16-2          
[161] later_1.3.2               viridisLite_0.4.2        
[163] AnnotationDbi_1.64.1      GenomicAlignments_1.38.2 
[165] memoise_2.0.1             beeswarm_0.4.0           
[167] tximport_1.28.0           cluster_2.1.6            
[169] timechange_0.3.0          globals_0.16.3